Neutrino mass constraint from an Implicit Likelihood Analysis of BOSS voids

TL;DR

This paper uses cosmic void statistics and an implicit likelihood approach to place new constraints on the sum of neutrino masses from galaxy survey data, highlighting the potential of voids in cosmological parameter estimation.

Contribution

It introduces a novel implicit likelihood inference method applied to void and galaxy clustering data for neutrino mass constraints, utilizing simulation-based modeling.

Findings

Upper bounds on neutrino mass sum of 0.43 and 0.35 eV at 95% credible interval.

Void statistics may be more effective at constraining neutrino mass from below.

Confirmed the Poisson distribution assumption for the void size function.

Abstract

Cosmic voids identified in the spatial distribution of galaxies provide complementary information to two-point statistics. In particular, constraints on the neutrino mass sum, $\sum m_\nu$, promise to benefit from the inclusion of void statistics. We perform inference on the CMASS NGC sample of SDSS-III/BOSS with the aim of constraining $\sum m_\nu$. We utilize the void size function, the void galaxy cross power spectrum, and the galaxy auto power spectrum. To extract constraints from these summary statistics we use a simulation-based approach, specifically implicit likelihood inference. We populate approximate gravity-only, particle neutrino cosmological simulations with an expressive halo occupation distribution model. With a conservative scale cut of kmax=0.15 h/Mpc and a Planck-inspired LCDM prior, we find upper bounds on $\sum m_\nu$ of 0.43 and 0.35 eV from the galaxy auto power spectrum and the full data vector, respectively (95% credible interval). We observe hints that the void statistics may be most effective at constraining $\sum m_\nu$ from below. We also substantiate the usual assumption that the void size function is Poisson distributed.